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Lightning-AI · Mar 15, 2024 · 71ca0b2 · 71ca0b2
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diff --git a/examples/bert_score-own_model.py b/examples/bert_score-own_model.py
@@ -79,13 +79,15 @@ def __call__(self, sentences: Union[str, List[str]], max_len: int = _MAX_LEN) ->
             sentence.lower().split()[:max_len] + [self.PAD_TOKEN] * (max_len - len(sentence.lower().split()))
             for sentence in sentences
         ]
-        output_dict["input_ids"] = torch.cat([
-            torch.cat([self.word2vec[word] for word in sentence]).unsqueeze(0) for sentence in tokenized_sentences
-        ])
-        output_dict["attention_mask"] = torch.cat([
-            torch.tensor([1 if word != self.PAD_TOKEN else 0 for word in sentence]).unsqueeze(0)
-            for sentence in tokenized_sentences
-        ]).long()
+        output_dict["input_ids"] = torch.cat(
+            [torch.cat([self.word2vec[word] for word in sentence]).unsqueeze(0) for sentence in tokenized_sentences]
+        )
+        output_dict["attention_mask"] = torch.cat(
+            [
+                torch.tensor([1 if word != self.PAD_TOKEN else 0 for word in sentence]).unsqueeze(0)
+                for sentence in tokenized_sentences
+            ]
+        ).long()
 
         return output_dict
 

diff --git a/src/torchmetrics/classification/accuracy.py b/src/torchmetrics/classification/accuracy.py
@@ -504,11 +504,13 @@ def __new__(  # type: ignore[misc]
         """Initialize task metric."""
         task = ClassificationTask.from_str(task)
 
-        kwargs.update({
-            "multidim_average": multidim_average,
-            "ignore_index": ignore_index,
-            "validate_args": validate_args,
-        })
+        kwargs.update(
+            {
+                "multidim_average": multidim_average,
+                "ignore_index": ignore_index,
+                "validate_args": validate_args,
+            }
+        )
 
         if task == ClassificationTask.BINARY:
             return BinaryAccuracy(threshold, **kwargs)

diff --git a/src/torchmetrics/classification/exact_match.py b/src/torchmetrics/classification/exact_match.py
@@ -406,11 +406,13 @@ def __new__(
     ) -> Metric:
         """Initialize task metric."""
         task = ClassificationTaskNoBinary.from_str(task)
-        kwargs.update({
-            "multidim_average": multidim_average,
-            "ignore_index": ignore_index,
-            "validate_args": validate_args,
-        })
+        kwargs.update(
+            {
+                "multidim_average": multidim_average,
+                "ignore_index": ignore_index,
+                "validate_args": validate_args,
+            }
+        )
         if task == ClassificationTaskNoBinary.MULTICLASS:
             if not isinstance(num_classes, int):
                 raise ValueError(f"`num_classes` is expected to be `int` but `{type(num_classes)} was passed.`")

diff --git a/src/torchmetrics/classification/f_beta.py b/src/torchmetrics/classification/f_beta.py
@@ -1075,11 +1075,13 @@ def __new__(
         """Initialize task metric."""
         task = ClassificationTask.from_str(task)
         assert multidim_average is not None  # noqa: S101  # needed for mypy
-        kwargs.update({
-            "multidim_average": multidim_average,
-            "ignore_index": ignore_index,
-            "validate_args": validate_args,
-        })
+        kwargs.update(
+            {
+                "multidim_average": multidim_average,
+                "ignore_index": ignore_index,
+                "validate_args": validate_args,
+            }
+        )
         if task == ClassificationTask.BINARY:
             return BinaryFBetaScore(beta, threshold, **kwargs)
         if task == ClassificationTask.MULTICLASS:
@@ -1138,11 +1140,13 @@ def __new__(
         """Initialize task metric."""
         task = ClassificationTask.from_str(task)
         assert multidim_average is not None  # noqa: S101  # needed for mypy
-        kwargs.update({
-            "multidim_average": multidim_average,
-            "ignore_index": ignore_index,
-            "validate_args": validate_args,
-        })
+        kwargs.update(
+            {
+                "multidim_average": multidim_average,
+                "ignore_index": ignore_index,
+                "validate_args": validate_args,
+            }
+        )
         if task == ClassificationTask.BINARY:
             return BinaryF1Score(threshold, **kwargs)
         if task == ClassificationTask.MULTICLASS:

diff --git a/src/torchmetrics/classification/hamming.py b/src/torchmetrics/classification/hamming.py
@@ -508,11 +508,13 @@ def __new__(  # type: ignore[misc]
         """Initialize task metric."""
         task = ClassificationTask.from_str(task)
         assert multidim_average is not None  # noqa: S101  # needed for mypy
-        kwargs.update({
-            "multidim_average": multidim_average,
-            "ignore_index": ignore_index,
-            "validate_args": validate_args,
-        })
+        kwargs.update(
+            {
+                "multidim_average": multidim_average,
+                "ignore_index": ignore_index,
+                "validate_args": validate_args,
+            }
+        )
         if task == ClassificationTask.BINARY:
             return BinaryHammingDistance(threshold, **kwargs)
         if task == ClassificationTask.MULTICLASS:

diff --git a/src/torchmetrics/classification/precision_recall.py b/src/torchmetrics/classification/precision_recall.py
@@ -945,11 +945,13 @@ def __new__(
     ) -> Metric:
         """Initialize task metric."""
         assert multidim_average is not None  # noqa: S101  # needed for mypy
-        kwargs.update({
-            "multidim_average": multidim_average,
-            "ignore_index": ignore_index,
-            "validate_args": validate_args,
-        })
+        kwargs.update(
+            {
+                "multidim_average": multidim_average,
+                "ignore_index": ignore_index,
+                "validate_args": validate_args,
+            }
+        )
         task = ClassificationTask.from_str(task)
         if task == ClassificationTask.BINARY:
             return BinaryPrecision(threshold, **kwargs)
@@ -1011,11 +1013,13 @@ def __new__(
         """Initialize task metric."""
         task = ClassificationTask.from_str(task)
         assert multidim_average is not None  # noqa: S101  # needed for mypy
-        kwargs.update({
-            "multidim_average": multidim_average,
-            "ignore_index": ignore_index,
-            "validate_args": validate_args,
-        })
+        kwargs.update(
+            {
+                "multidim_average": multidim_average,
+                "ignore_index": ignore_index,
+                "validate_args": validate_args,
+            }
+        )
         if task == ClassificationTask.BINARY:
             return BinaryRecall(threshold, **kwargs)
         if task == ClassificationTask.MULTICLASS:

diff --git a/src/torchmetrics/classification/specificity.py b/src/torchmetrics/classification/specificity.py
@@ -492,11 +492,13 @@ def __new__(  # type: ignore[misc]
         """Initialize task metric."""
         task = ClassificationTask.from_str(task)
         assert multidim_average is not None  # noqa: S101  # needed for mypy
-        kwargs.update({
-            "multidim_average": multidim_average,
-            "ignore_index": ignore_index,
-            "validate_args": validate_args,
-        })
+        kwargs.update(
+            {
+                "multidim_average": multidim_average,
+                "ignore_index": ignore_index,
+                "validate_args": validate_args,
+            }
+        )
         if task == ClassificationTask.BINARY:
             return BinarySpecificity(threshold, **kwargs)
         if task == ClassificationTask.MULTICLASS:

diff --git a/src/torchmetrics/classification/stat_scores.py b/src/torchmetrics/classification/stat_scores.py
@@ -531,11 +531,13 @@ def __new__(
         """Initialize task metric."""
         task = ClassificationTask.from_str(task)
         assert multidim_average is not None  # noqa: S101  # needed for mypy
-        kwargs.update({
-            "multidim_average": multidim_average,
-            "ignore_index": ignore_index,
-            "validate_args": validate_args,
-        })
+        kwargs.update(
+            {
+                "multidim_average": multidim_average,
+                "ignore_index": ignore_index,
+                "validate_args": validate_args,
+            }
+        )
         if task == ClassificationTask.BINARY:
             return BinaryStatScores(threshold, **kwargs)
         if task == ClassificationTask.MULTICLASS:

diff --git a/src/torchmetrics/functional/clustering/dunn_index.py b/src/torchmetrics/functional/clustering/dunn_index.py
@@ -39,9 +39,9 @@ def _dunn_index_update(data: Tensor, labels: Tensor, p: float) -> Tuple[Tensor,
         torch.stack([a - b for a, b in combinations(centroids, 2)], dim=0), ord=p, dim=1
     )
 
-    max_intracluster_distance = torch.stack([
-        torch.linalg.norm(ci - mu, ord=p, dim=1).max() for ci, mu in zip(clusters, centroids)
-    ])
+    max_intracluster_distance = torch.stack(
+        [torch.linalg.norm(ci - mu, ord=p, dim=1).max() for ci, mu in zip(clusters, centroids)]
+    )
 
     return intercluster_distance, max_intracluster_distance
 

diff --git a/src/torchmetrics/functional/clustering/utils.py b/src/torchmetrics/functional/clustering/utils.py
@@ -154,10 +154,12 @@ def calculate_contingency_matrix(
     num_classes_target = target_classes.size(0)
 
     contingency = torch.sparse_coo_tensor(
-        torch.stack((
-            target_idx,
-            preds_idx,
-        )),
+        torch.stack(
+            (
+                target_idx,
+                preds_idx,
+            )
+        ),
         torch.ones(target_idx.shape[0], dtype=preds_idx.dtype, device=preds_idx.device),
         (
             num_classes_target,

diff --git a/src/torchmetrics/functional/text/helper_embedding_metric.py b/src/torchmetrics/functional/text/helper_embedding_metric.py
@@ -249,9 +249,9 @@ def _get_tokens_idf(self) -> Dict[int, float]:
             token_counter.update(tokens)
 
         tokens_idf: Dict[int, float] = defaultdict(self._get_tokens_idf_default_value)
-        tokens_idf.update({
-            idx: math.log((self.num_sentences + 1) / (occurrence + 1)) for idx, occurrence in token_counter.items()
-        })
+        tokens_idf.update(
+            {idx: math.log((self.num_sentences + 1) / (occurrence + 1)) for idx, occurrence in token_counter.items()}
+        )
         return tokens_idf
 
     def _get_tokens_idf_default_value(self) -> float:

diff --git a/src/torchmetrics/wrappers/multitask.py b/src/torchmetrics/wrappers/multitask.py
@@ -217,14 +217,14 @@ def clone(self, prefix: Optional[str] = None, postfix: Optional[str] = None) ->
         multitask_copy = deepcopy(self)
         if prefix is not None:
             prefix = self._check_arg(prefix, "prefix")
-            multitask_copy.task_metrics = nn.ModuleDict({
-                prefix + key: value for key, value in multitask_copy.task_metrics.items()
-            })
+            multitask_copy.task_metrics = nn.ModuleDict(
+                {prefix + key: value for key, value in multitask_copy.task_metrics.items()}
+            )
         if postfix is not None:
             postfix = self._check_arg(postfix, "postfix")
-            multitask_copy.task_metrics = nn.ModuleDict({
-                key + postfix: value for key, value in multitask_copy.task_metrics.items()
-            })
+            multitask_copy.task_metrics = nn.ModuleDict(
+                {key + postfix: value for key, value in multitask_copy.task_metrics.items()}
+            )
         return multitask_copy
 
     def plot(

diff --git a/tests/integrations/test_lightning.py b/tests/integrations/test_lightning.py
@@ -358,10 +358,12 @@ class TestModel(BoringModel):
         def __init__(self) -> None:
             super().__init__()
             self.multitask = MultitaskWrapper({"classification": BinaryAccuracy(), "regression": MeanSquaredError()})
-            self.multitask_collection = MultitaskWrapper({
-                "classification": MetricCollection([BinaryAccuracy(), BinaryAveragePrecision()]),
-                "regression": MetricCollection([MeanSquaredError(), MeanAbsoluteError()]),
-            })
+            self.multitask_collection = MultitaskWrapper(
+                {
+                    "classification": MetricCollection([BinaryAccuracy(), BinaryAveragePrecision()]),
+                    "regression": MetricCollection([MeanSquaredError(), MeanAbsoluteError()]),
+                }
+            )
 
             self.accuracy = BinaryAccuracy()
             self.mse = MeanSquaredError()

diff --git a/tests/unittests/bases/test_collections.py b/tests/unittests/bases/test_collections.py
@@ -580,10 +580,12 @@ def test_classwise_wrapper_compute_group():
 
 def test_compute_on_different_dtype():
     """Check that extraction of compute groups are robust towards difference in dtype."""
-    m = MetricCollection([
-        MulticlassConfusionMatrix(num_classes=3),
-        MulticlassMatthewsCorrCoef(num_classes=3),
-    ])
+    m = MetricCollection(
+        [
+            MulticlassConfusionMatrix(num_classes=3),
+            MulticlassMatthewsCorrCoef(num_classes=3),
+        ]
+    )
     assert not m._groups_checked
     assert m.compute_groups == {0: ["MulticlassConfusionMatrix"], 1: ["MulticlassMatthewsCorrCoef"]}
     preds = torch.randn(10, 3).softmax(dim=-1)
@@ -625,14 +627,18 @@ def test_error_on_wrong_specified_compute_groups():
             ),
         ],
         {
-            "macro": MetricCollection([
-                MulticlassAccuracy(num_classes=3, average="macro"),
-                MulticlassPrecision(num_classes=3, average="macro"),
-            ]),
-            "micro": MetricCollection([
-                MulticlassAccuracy(num_classes=3, average="micro"),
-                MulticlassPrecision(num_classes=3, average="micro"),
-            ]),
+            "macro": MetricCollection(
+                [
+                    MulticlassAccuracy(num_classes=3, average="macro"),
+                    MulticlassPrecision(num_classes=3, average="macro"),
+                ]
+            ),
+            "micro": MetricCollection(
+                [
+                    MulticlassAccuracy(num_classes=3, average="micro"),
+                    MulticlassPrecision(num_classes=3, average="micro"),
+                ]
+            ),
         },
     ],
 )

diff --git a/tests/unittests/clustering/test_dunn_index.py b/tests/unittests/clustering/test_dunn_index.py
@@ -38,9 +38,9 @@ def _reference_np_dunn_index(data, labels, p):
         np.stack([a - b for a, b in combinations(centroids, 2)], axis=0), ord=p, axis=1
     )
 
-    max_intracluster_distance = np.stack([
-        np.linalg.norm(ci - mu, ord=p, axis=1).max() for ci, mu in zip(clusters, centroids)
-    ])
+    max_intracluster_distance = np.stack(
+        [np.linalg.norm(ci - mu, ord=p, axis=1).max() for ci, mu in zip(clusters, centroids)]
+    )
 
     return intercluster_distance.min() / max_intracluster_distance.max()
 

diff --git a/tests/unittests/detection/test_intersection.py b/tests/unittests/detection/test_intersection.py
@@ -76,20 +76,24 @@ def _tv_wrapper_class(preds, target, base_fn, respect_labels, iou_threshold, cla
 
 
 _preds_fn = (
-    torch.tensor([
-        [296.55, 93.96, 314.97, 152.79],
-        [328.94, 97.05, 342.49, 122.98],
-        [356.62, 95.47, 372.33, 147.55],
-    ])
+    torch.tensor(
+        [
+            [296.55, 93.96, 314.97, 152.79],
+            [328.94, 97.05, 342.49, 122.98],
+            [356.62, 95.47, 372.33, 147.55],
+        ]
+    )
     .unsqueeze(0)
     .repeat(4, 1, 1)
 )
 _target_fn = (
-    torch.tensor([
-        [300.00, 100.00, 315.00, 150.00],
-        [330.00, 100.00, 350.00, 125.00],
-        [350.00, 100.00, 375.00, 150.00],
-    ])
+    torch.tensor(
+        [
+            [300.00, 100.00, 315.00, 150.00],
+            [330.00, 100.00, 350.00, 125.00],
+            [350.00, 100.00, 375.00, 150.00],
+        ]
+    )
     .unsqueeze(0)
     .repeat(4, 1, 1)
 )